scholarly journals Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xunlin Jiang ◽  
Haifeng Ling ◽  
Jun Yan ◽  
Bo Li ◽  
Zhao Li
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Energy Use ◽  
Particle Swarm ◽  
Electrical Energy ◽  
Pso Algorithm ◽  
Equipment Maintenance ◽  
Electrical Energy Consumption ◽  
Swarm Optimization

Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN) and particle swarm optimization (PSO). A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, and a new adaptive PSO algorithm is proposed for optimizing the parameters of the ANN. Experimental results demonstrate that our approach provides much better accuracies than some other competitive methods on the test data.

scholarly journals Position Control of Ultrasonic Motor Using IMC-PID Combined with Tribes Type NN Algorithm

2011 ◽  
Vol 2-3 ◽  
pp. 12-17
Author(s):  
Sheng Lin Mu ◽  
Kanya Tanaka
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Pid Control ◽  
Position Control ◽  
Back Propagation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ultrasonic Motor ◽  
Swarm Optimization

In this paper, we propose a novel scheme of IMC-PID control combined with a tribes type neural network (NN) for the position control of ultrasonic motor (USM). In this method, the NN controller is employed for tuning the parameter in IMC-PID control. The weights of NN are designed to be updated by the tribes-particle swarm optimization (PSO) algorithm. This method makes it possible to compensate for the characteristic changes and nonlinearity of USM. The parameter-free tribes-PSO requires no information about the USM beforehand; hence its application overcomes the problem of Jacobian estimation in the conventional back propagation (BP) method of NN. The effectiveness of the proposed method is confirmed by experiments.

WNN Model Based on Particle Swarm Optimization for Fault Diagnosis in Analog Circuit

2013 ◽  
Vol 427-429 ◽  
pp. 1048-1051
Author(s):  
Xu Sheng Gan ◽  
Hao Lin Cui ◽  
Ya Rong Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Convergence Rate ◽  
Analog Circuit ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Swarm Optimization ◽  
Fast Convergence Rate

In order to diagnose the fault in analog circuit correctly, a Wavelet Neural Network (WNN) method is proposed that uses the Particle Swarm Optimization (PSO) algorithm to optimize the network parameters. For the improvement of convergence rate in WNN based on PSO algorithm, a compressing method in research space is introduced into the traditional PSO algorithm to improve the convergence in WNN training. The simulation shows that the proposed method has a good diagnosis with fast convergence rate for the fault in analog circuit.

scholarly journals Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5609 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Deep Learning Neural Network

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

Monopole-Gear Design Based on Neural Network and Modified Particle Swarm Optimization

2013 ◽  
Vol 477-478 ◽  
pp. 368-373 ◽  
Author(s):  
Hai Rong Fang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Results ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Gear Design ◽  
Annealing Algorithm

In order to raise the design efficiency and get the most excellent design effect, this paper combined Particle Swarm Optimization (PSO) algorithm and put forward a new kind of neural network, which based on PSO algorithm, and the implementing framework of PSO and NARMA model. It gives the basic theory, steps and algorithm; The test results show that rapid global convergence and reached the lesser mean square error MSE) when compared with Genetic Algorithm, Simulated Annealing Algorithm, the BP algorithm with momentum term.

A Hybrid Method Based on SVM Integrated Improved PSO Algorithm for Electrical Energy Consumption Forecasting of Crude Oil Pipeline

2020 ◽  
Author(s):  
Lei Xu ◽  
Lei Hou ◽  
Zhenyu Zhu ◽  
Yu Li ◽  
Ting Lei
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Crude Oil ◽  
Electrical Energy ◽  
Support Vector ◽  
Electrical Energy Consumption ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Oil Pipelines

Abstract Electrical energy consumption forecasting of crude oil pipelines plays a critical role in energy consumption target setting, batch scheduling, and unit commitment. For actual crude oil pipelines, because of its uncertainty, nonlinearity, intermittency, fluctuations and complexity, it is challenging to establish the electrical energy consumption forecasting model. And it is difficult to describe the non-linear characteristics of electrical energy consumption forecasting by traditional methods. Therefore, a novel hybrid electrical energy consumption forecasting system based on the combination of support vector machine (SVM) and improved particle swarm optimization (IPSO) is proposed, which includes four parts: data pre-processing part, optimization part, forecasting part, and evaluation part. In the pre-processing stage, in order to avoid large deviation caused by sampling stochasticity of small samples, the training set and the test set are divided by stratified sampling method. During the modeling process, the non-linear relationship in electrical energy consumption forecasting is efficiently represented by support vector machine, and the parameters of support vector machine regression are optimized by the improved particle swarm optimization algorithm. According to the established IPSO-SVM model, evaluation part is conducted to make a comprehensive evaluation for this framework. By comparing the evaluation indicators of IPSO-SVM with that of eight state-of-the-art forecasting methods, the effectiveness of IPSO-SVM method is evaluated. Based on the operation data of four crude oil pipelines in China, the results show that the proposed IPSO-SVM hybrid model has the best forecasting performance than other benchmark models, and its forecasting results are the closest to the actual data. It is concluded that the proposed approach can be an efficient technique for electrical energy consumption forecasting of crude oil pipelines.

Neural Network Assisted Particle Swarm Optimization of Machining Process

2013 ◽  
Vol 581 ◽  
pp. 511-516
Author(s):  
Uros Zuperl ◽  
Franci Cus
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Machining Process ◽  
Neural Network Modeling ◽  
Swarm Optimization ◽  
Turning Operation ◽  
Optimization System

In this paper, optimization system based on the artificial neural networks (ANN) and particle swarm optimization (PSO) algorithm was developed for the optimization of machining parameters for turning operation. The optimization system integrates the neural network modeling of the objective function and particle swarm optimization of turning parameters. New neural network assisted PSO algorithm is explained in detail. An objective function based on maximum profit, minimum costs and maximum cutting quality in turning operation has been used. This paper also exhibits the efficiency of the proposed optimization over the genetic algorithms (GA), ant colony optimization (ACO) and simulated annealing (SA).

On PSO Based BP Neural Network

2014 ◽  
Vol 602-605 ◽  
pp. 3518-3521
Author(s):  
Peng Hu ◽  
Xiao Quan Song
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Better Than

Particle swarm optimization (PSO) based BP neural network is introduced , which is superior to the traditional BP neural network . The traditional BP neural network and PSO algorithm is illustrated respectively, and introduces how to apply PSO algorithm in BP neural network. The numerical simulation proves that PSO-BP neural network performs better than traditional BP neural network.

Multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

2021 ◽  
pp. 1-12
Author(s):  
Lan Zhang ◽  
Lei Xu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Short Term ◽  
Swarm Optimization ◽  
Load Forecast ◽  
Multi Objective ◽  
Recursive Neural Network

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

scholarly journals Penerapan Particle Swarm Optimization Pada Feedforward Neural Network Untuk Klasifikasi Teks Hadis Bukhari Terjemahan Bahasa Indonesia

2018 ◽  
Vol 2 (4) ◽  
pp. 165
Author(s):  
Muhammad Ghufran ◽  
Adiwijaya Adiwijaya ◽  
Said Al-Faraby
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Islamic Law ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Feedforward Neural Network ◽  
General Technique ◽  
Swarm Optimization ◽  
The Public ◽  
Speed Up

Hadith is the second source of Islamic law after Al-Qur'an and used as a guide for Muslims life. there are many hadith which has been narrated, one of them is Bukhari history. This research aims to build a model that can classify Bukhari hadith translation of Indonesian language. This topic is chosen to assist the public in understanding the meaning of the information that contained in the hadith, in the form of advocacy information, prohibitions or just information. The Backpropagation Algorithm (BP) is the general technique that used to train the Feedforward Neural Network (FNN) in classification process cause it has good accuracy for text classification. But, BP has a weakness that is relatively slow to reach convergent and stuck in local minimum. To overcome this, the Particle Swarm Optimization (PSO) algorithm is used to speed up convergence and find the minimum global value. The purpose of this test is to see the PSO's ability to train the weight and refraction of FNN. The result of this research on 1000 hadith data show that model PSO-FNN with stemming process get 88.5% accuracy while without stemming process get 88.57% accuracy. Meanwhile, the result of comparative test between PSO-FNN with BP-FNN, the result shows that  PSO-FNN get accuracy equal to 88.57% which is lower 0.93% than BP-FNN which has 89.5% accuracy.

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